本期导读
主题
AI、工业 5.0、物联网、双层优化、图像融合、故障预警、区块链、过程监控、细胞免疫疗法、神经自适应控制、网络攻击、信息物理系统、离散事件系统、监督控制、事件触发机制、扩散模型、时间序列、深度强化学习、神经网络...
全球科研机构
英国University of Surrey、University of Leeds；新加坡Nanyang Technological University；日本Tokyo University of Agriculture and Technology；中国科学院自动化研究所、上海交通大学、北京航空航天大学、北京理工大学、哈尔滨工业大学、东北大学、西安交通大学、大连理工大学、华东理工大学、北京科技大学、北京化工大学、山东大学、安徽大学、东南大学、西南大学、重庆大学...
L. Fu, S. Ling, D. Wu, M. Kang, F.-Y. Wang, and H. Sun, “Parallel seeds: From foundation models to foundation intelligence for agricultural sustainability,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 481–484, Mar. 2025. doi: 10.1109/JAS.2024.124914
J. Xu, Q. Sun, Q.-L. Han, and Y. Tang, “When embodied AI meets Industry 5.0: Human-centered smart manufacturing,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 485–501, Mar. 2025. doi: 10.1109/JAS.2025.125327
> Presents a comprehensive framework for smart manufacturing based on Industry 5.0 principles, providing insights into how human-centric, sustainable, and resilient manufacturing systems can be achieved.
> An in-depth investigation is conducted on recent advancements in embodied AI, highlighting state-of-the-art techniques such as visual active perception, embodied interaction, and sim-to-real robotic control.
> Several research challenges and potential directions for future studies are proposed, including the task decomposition from human instructions, the communication models for heterogeneous multi-agent systems, and the self-correction methods.
J. Liu, X. Li, Z. Wang, Z. Jiang, W. Zhong, W. Fan, and B. Xu, “PromptFusion: Harmonized semantic prompt learning for infrared and visible image fusion,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 502–515, Mar. 2025. doi: 10.1109/JAS.2024.124878
> Propose PromptFusion, a semantic-guided fusion method that leverages textual prompts to bridge the semantic gaps between modalities, improving machine perception while preserving visual fidelity.
> A frequency-aware spectra encoder that decomposes and aggregates the low- and high-pass subbands from infrared and visible images to improve the multi-modality feature integration.
> Developed a two-stage prompt learning framework that uses task-specific design prompts to constrain the fusion process, accurately distinguishing the targets and scenes by learning typical characteristics of modalities.
Y. Xu and M. Deng, “Nonlinear control for unstable networked plants in the presence of actuator and sensor limitations using robust right coprime factorization,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 516–527, Mar. 2025. doi: 10.1109/JAS.2024.124854
> Robust right coprime factorization of an unstable networked plant is employed to achieve BIBO stability.
> A second-DoF switched stabilizer in the feedback loop satisfying the perturbed Bezout identity and a robustness condition is designed to deal with the effect of actuator limitations.
> An identity operator definition is implemented to compensate for network-induced sensor limitations in the form of time-varying delay and sensor noise.
X. Li, X. Ban, H. Qiao, Z. Yuan, H.-N. Dai, C. Yao, Y. Guo, M. Obaidat, and G. Huang, “Multi-scale time series segmentation network based on Eddy current testing for detecting surface metal defects,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 528–538, Mar. 2025. doi: 10.1109/JAS.2025.125117
> New time-series method improves eddy current testing for metal defect detection.
> Handles varying defect sizes and depths with a multi-scale network.
> Define the eddy current testing as a time series semantic segmentation problem.
Y. Gao, S. Qiu, M. Liu, L. Zhang, and X. Cao, “Fault warning of satellite momentum wheels with a lightweight transformer improved by FastDTW,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 539–549, Mar. 2025. doi: 10.1109/JAS.2024.124689
> Proposes an AMB-MIC correlation analysis method which calculates the correlation between various telemetry data. Compared with previous methods.
> Proposes a lightweight Transformer, named as ADTWformer.
> Establishes a robust health number alarm mechanism.
Y. Chen, F. Lin, Z. Chen, C. Tang, and C. Chen, “Optimal production capacity matching for blockchain-enabled manufacturing collaboration with the iterative double auction method,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 550–562, Mar. 2025. doi: 10.1109/JAS.2024.124626
> A blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchain-based P2P collaboration.
> A digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain.
> An optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants.
X. Ma, T. Chen, and Y. Wang, “Dynamic process monitoring based on dot product feature analysis for thermal power plants,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 563–574, Mar. 2025. doi: 10.1109/JAS.2024.124908
> Stronger Dynamic Information Analysis Capability: DPFA uses the dot product operation to find the relationship between the current sample and the past sample, which directly reflects the dynamic characteristics of the data.
> Lower Computational Complexity: By comparison, the online calculation complexity of DPFA is lower than those of classic dynamic algorithms and even lower than those of some static algorithms such as PCA and SFA, when the order is smaller than the sample dimension.
J. Sun, D. Li, H. Zhang, L. Liu, and W. Zhao, “Consensus control strategy for the treatment of tumour with neuroadaptive cellular immunotherapy,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 575–584, Mar. 2025. doi: 10.1109/JAS.2024.124941
> Dynamic change model of cancer cells constructed can be simplified and considered as a second-order system model.
> A novel model of expected cancer cell change trajectory is designed.
> Cellular immunotherapy scheduling is considered under the backstepping method for the first time.
Z. He, N. Wu, R. Su, and Z. Li, “Cyber-attacks with resource constraints on discrete event systems under supervisory control,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 585–595, Mar. 2025. doi: 10.1109/JAS.2024.124596
> Proposes a property of a closed-loop system to formalize whether the system reaches an unsafe state after receiving attacks with resource constraints.
> An algorithm is provided to model a corrupted supervisor under covert sensor and actuator attacks with resource constraints.
> A polynomial-time method is developed to verify the proposed property based on a plant model, a corrupted supervisor, and the initial resource of an attacker.
C. Gu, X. Wang, K. Li, X. Yin, S. Li, and L. Wang, “Enhanced tube-based event-triggered stochastic model predictive control with additive uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 596–605, Mar. 2025. doi: 10.1109/JAS.2024.124974
> An event-triggered SMPC is proposed for LTI system with additive uncertainties.
> Two parameters are designed to adjust the triggering frequency.
> Feasibility probability and almost surely asymptotically stability are proved.
B. Lu, Q. Miao, Y. Liu, T. Tamir, H. Zhao, X. Zhang, Y. Lv, and F.-Y. Wang, “A diffusion model for traffic data imputation,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 606–617, Mar. 2025. doi: 10.1109/JAS.2024.124611
> Propose an Implicit-Explicit Diffusion Model for traffic data imputation.
> Capture hidden multiscale dependencies with an implicit extraction module.
> Fuse implicit and explicit features for enhanced traffic data imputation.
X. Liang, Q. Wu, Y. Zhou, C. Tan, H. Yin, and C. Sun, “Spiking reinforcement learning enhanced by bioinspired event source of multi-dendrite spiking neuron and dynamic thresholds,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 618–629, Mar. 2025. doi: 10.1109/JAS.2024.124551
> Compared to DNNs, utilizing SNNs to simulate neuronal dynamics for optimizing decision tasks is more biologically plausible.
> Proposes a MDSN model，expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane potential.
> Applying the MDSN to deep distributional reinforcement learning enhances its performance in executing complex decision-making tasks.
X. Sun, H. Du, W. Chen, and W. Zhu, “Distributed finite-time formation control of multiple mobile robot systems without global information,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 630–632, Mar. 2025. doi: 10.1109/JAS.2023.123981
M. Chen, L. Tao, J. Lou, and X. Luo, “Latent-factorization-of-tensors-incorporated battery cycle life prediction,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 633–635, Mar. 2025. doi: 10.1109/JAS.2024.124602
C. Liu, Q. Gao, W. Wang, and J. Lü, “Distributed cooperative regulation for networked re-entrant manufacturing systems,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 636–638, Mar. 2025. doi: 10.1109/JAS.2024.124728
L. Fan, X. Chen, S. Li, and Y. Chai, “Multi-interval-aggregation failure point approximation for remaining useful life prediction,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 639–641, Mar. 2025. doi: 10.1109/JAS.2024.124593
S. Shen, R. Chai, Y. Xia, and S. Chai, “Resilient nonlinear MPC with a dynamic event-triggered strategy under DoS attacks,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 642–644, Mar. 2025. doi: 10.1109/JAS.2024.124851
W. Zhang, H. Hao, and Y. Zhang, “State of charge prediction of lithium-ion batteries for electric aircraft with swin transformer,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 645–647, Mar. 2025. doi: 10.1109/JAS.2023.124020
R. Li, Y. Tan, X. Su, and J. Huang, “A verification theorem for feedback Nash equilibrium in multiple-player nonzero-sum impulse game,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 648–650, Mar. 2025. doi: 10.1109/JAS.2024.124752
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